$$\mathsf {ELSA}$$ : Efficient Long-Term Secure Storage of Large Datasets

2019 ◽  
pp. 269-286 ◽  
Author(s):  
Matthias Geihs ◽  
Johannes Buchmann
Keyword(s):  
Large Datasets ◽  
Secure Storage

scholarly journals ELSA: efficient long-term secure storage of large datasets (full version) ∗

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Philipp Muth ◽  
Matthias Geihs ◽  
Tolga Arul ◽  
Johannes Buchmann ◽  
Stefan Katzenbeisser
Keyword(s):  
Large Datasets ◽  
Full Version ◽  
Secure Storage

scholarly journals Proceedings of the First Curing Coma Campaign NIH Symposium: Challenging the Future of Research for Coma and Disorders of Consciousness

2021 ◽  
Vol 35 (S1) ◽  
pp. 4-23
Author(s):  
Jan Claassen ◽  
Yama Akbari ◽  
Sheila Alexander ◽  
Mary Kay Bader ◽  
Kathleen Bell ◽  
...  
Keyword(s):  
Neurocritical Care ◽  
Panel Discussion ◽  
Large Datasets ◽  
Disorders Of Consciousness ◽  
National Institutes Of Health ◽  
Proof Of Concept ◽  
Research Gaps ◽  
Main Research ◽  
The World

AbstractComa and disorders of consciousness (DoC) are highly prevalent and constitute a burden for patients, families, and society worldwide. As part of the Curing Coma Campaign, the Neurocritical Care Society partnered with the National Institutes of Health to organize a symposium bringing together experts from all over the world to develop research targets for DoC. The conference was structured along six domains: (1) defining endotype/phenotypes, (2) biomarkers, (3) proof-of-concept clinical trials, (4) neuroprognostication, (5) long-term recovery, and (6) large datasets. This proceedings paper presents actionable research targets based on the presentations and discussions that occurred at the conference. We summarize the background, main research gaps, overall goals, the panel discussion of the approach, limitations and challenges, and deliverables that were identified.

Medical Data Analytics in the Cloud Using Homomorphic Encryption

2016 ◽  
pp. 751-768
Author(s):  
Övünç Kocabaş ◽  
Tolga Soyata
Keyword(s):  
Health Monitoring ◽  
Data Privacy ◽  
Homomorphic Encryption ◽  
Quality Of Healthcare ◽  
Patient Data ◽  
Personal Health Information ◽  
Healthcare Applications ◽  
Phase 3 ◽  
Secure Storage

Transitioning US healthcare into the digital era is necessary to reduce operational costs at Healthcare Organizations (HCO) and provide better diagnostic tools for healthcare professionals by making digital patient data available in a timely fashion. Such a transition requires that the Personal Health Information (PHI) is protected in three different phases of the manipulation of digital patient data: 1) Acquisition, 2) Storage, and 3) Computation. While being able to perform analytics or using such PHI for long-term health monitoring can have significant positive impacts on the quality of healthcare, securing PHI in each one of these phases presents unique challenges in each phase. While established encryption techniques, such as Advanced Encryption Standard (AES), can secure PHI in Phases 1 (acquisition) and 2 (storage), they can only assure secure storage. Assuring the data privacy in Phase 3 (computation) is much more challenging, since there exists no method to perform computations, such as analytics and long-term health monitoring, on encrypted data efficiently. In this chapter, the authors study one emerging encryption technique, called Fully Homomorphic Encryption (FHE), as a candidate to perform secure analytics and monitoring on PHI in Phase 3. While FHE is in its developing stages and a mainstream application of it to general healthcare applications may take years to be established, the authors conduct a feasibility study of its application to long-term patient monitoring via cloud-based ECG data acquisition through existing ECG acquisition devices.

scholarly journals Bats are able to maintain long-term social relationships despite the high fission–fusion dynamics of their groups

2011 ◽  
Vol 278 (1719) ◽  
pp. 2761-2767 ◽  
Author(s):  
Gerald Kerth ◽  
Nicolas Perony ◽  
Frank Schweitzer
Keyword(s):  
Social Relationships ◽  
Large Datasets ◽  
Social Structures ◽  
High Importance ◽  
Wild Mammals ◽  
The Social ◽  
Multi Level ◽  
Weighted Network Analysis ◽  
Term Data

Elephants, dolphins, as well as some carnivores and primates maintain social links despite their frequent splitting and merging in groups of variable composition, a phenomenon known as fission–fusion. Information on the dynamics of social links and interactions among individuals is of high importance to the understanding of the evolution of animal sociality, including that of humans. However, detailed long-term data on such dynamics in wild mammals with fully known demography and kin structures are scarce. Applying a weighted network analysis on 20 500 individual roosting observations over 5 years, we show that in two wild Bechstein's bat colonies with high fission–fusion dynamics, individuals of different age, size, reproductive status and relatedness maintain long-term social relationships. In the larger colony, we detected two stable subunits, each comprising bats from several family lineages. Links between these subunits were mainly maintained by older bats and persisted over all years. Moreover, we show that the full details of the social structure become apparent only when large datasets are used. The stable multi-level social structures in Bechstein's bat colonies resemble that of elephants, dolphins and some primates. Our findings thus may shed new light on the link between social complexity and social cognition in mammals.

scholarly journals A Wearable Device for Online and Long-Term ECG Monitoring

2018 ◽  
Author(s):  
Marco Longoni ◽  
Diego Carrera ◽  
Beatrice Rossi ◽  
Pasqualina Fragneto ◽  
Marco Pessione ◽  
...  
Keyword(s):  
Sparse Coding ◽  
Online Monitoring ◽  
Large Datasets ◽  
Wearable Device ◽  
Ecg Monitoring ◽  
Ecg Signals ◽  
Everyday Activities ◽  
Long Term Monitoring ◽  
Term Monitoring

We present a prototype wearable device able to perform online and long-term monitoring of ECG signals, and detect anomalous heartbeats such as arrhythmias. Our solution is based on user-specific dictionaries which characterizes the morphology of normal heartbeats and are learned every time the device is positioned. Anomalies are detected via an optimized sparse coding procedure, which assesses the conformance of each heartbeat to the user-specific dictionary. The dictionaries are adapted during online monitoring, to track heart rate variations occurring during everyday activities. Perhaps surprisingly, dictionary adaptation can be successfully performed by transformations that are user-independent and learned from large datasets of ECG signals.

scholarly journals Findings from an in-Depth Annual Tree-Ring Radiocarbon Intercomparison

Radiocarbon ◽  
2020 ◽  
Vol 62 (4) ◽  
pp. 873-882 ◽  
Author(s):  
L Wacker ◽  
E M Scott ◽  
A Bayliss ◽  
D Brown ◽  
E Bard ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Tree Ring ◽  
High Precision ◽  
Calibration Curve ◽  
Accelerator Mass Spectrometry ◽  
Large Datasets ◽  
Accuracy And Precision ◽  
Short Period ◽  
Annual Resolution

ABSTRACTThe radiocarbon (14C) calibration curve so far contains annually resolved data only for a short period of time. With accelerator mass spectrometry (AMS) matching the precision of decay counting, it is now possible to efficiently produce large datasets of annual resolution for calibration purposes using small amounts of wood. The radiocarbon intercomparison on single-year tree-ring samples presented here is the first to investigate specifically possible offsets between AMS laboratories at high precision. The results show that AMS laboratories are capable of measuring samples of Holocene age with an accuracy and precision that is comparable or even goes beyond what is possible with decay counting, even though they require a thousand times less wood. It also shows that not all AMS laboratories always produce results that are consistent with their stated uncertainties. The long-term benefits of studies of this kind are more accurate radiocarbon measurements with, in the future, better quantified uncertainties.

Medical Data Analytics in the Cloud Using Homomorphic Encryption

2014 ◽  
pp. 471-488 ◽  
Author(s):  
Övünç Kocabaş ◽  
Tolga Soyata
Keyword(s):  
Health Monitoring ◽  
Data Privacy ◽  
Homomorphic Encryption ◽  
Quality Of Healthcare ◽  
Patient Data ◽  
Personal Health Information ◽  
Healthcare Applications ◽  
Phase 3 ◽  
Secure Storage

Transitioning US healthcare into the digital era is necessary to reduce operational costs at Healthcare Organizations (HCO) and provide better diagnostic tools for healthcare professionals by making digital patient data available in a timely fashion. Such a transition requires that the Personal Health Information (PHI) is protected in three different phases of the manipulation of digital patient data: 1) Acquisition, 2) Storage, and 3) Computation. While being able to perform analytics or using such PHI for long-term health monitoring can have significant positive impacts on the quality of healthcare, securing PHI in each one of these phases presents unique challenges in each phase. While established encryption techniques, such as Advanced Encryption Standard (AES), can secure PHI in Phases 1 (acquisition) and 2 (storage), they can only assure secure storage. Assuring the data privacy in Phase 3 (computation) is much more challenging, since there exists no method to perform computations, such as analytics and long-term health monitoring, on encrypted data efficiently. In this chapter, the authors study one emerging encryption technique, called Fully Homomorphic Encryption (FHE), as a candidate to perform secure analytics and monitoring on PHI in Phase 3. While FHE is in its developing stages and a mainstream application of it to general healthcare applications may take years to be established, the authors conduct a feasibility study of its application to long-term patient monitoring via cloud-based ECG data acquisition through existing ECG acquisition devices.

scholarly journals Automated long-term recording and analysis of neural activity in behaving animals

eLife ◽  
2017 ◽  
Vol 6 ◽  
Author(s):  
Ashesh K Dhawale ◽  
Rajesh Poddar ◽  
Steffen BE Wolff ◽  
Valentin A Normand ◽  
Evi Kopelowitz ◽  
...  
Keyword(s):  
Neural Activity ◽  
Neural Circuits ◽  
Large Datasets ◽  
Neural Dynamics ◽  
Single Unit Activity ◽  
Firing Rates ◽  
Freely Moving ◽  
Automated Platform ◽  
And Behavior

Addressing how neural circuits underlie behavior is routinely done by measuring electrical activity from single neurons in experimental sessions. While such recordings yield snapshots of neural dynamics during specified tasks, they are ill-suited for tracking single-unit activity over longer timescales relevant for most developmental and learning processes, or for capturing neural dynamics across different behavioral states. Here we describe an automated platform for continuous long-term recordings of neural activity and behavior in freely moving rodents. An unsupervised algorithm identifies and tracks the activity of single units over weeks of recording, dramatically simplifying the analysis of large datasets. Months-long recordings from motor cortex and striatum made and analyzed with our system revealed remarkable stability in basic neuronal properties, such as firing rates and inter-spike interval distributions. Interneuronal correlations and the representation of different movements and behaviors were similarly stable. This establishes the feasibility of high-throughput long-term extracellular recordings in behaving animals.

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